Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations87672
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 MiB
Average record size in memory160.0 B

Variable types

Text1
Numeric9
Categorical10

Alerts

day has constant value "99" Constant
hour has constant value "99" Constant
meas_flag_MLY-TAVG-NORMAL has constant value " " Constant
meas_flag_MLY-TMAX-NORMAL has constant value " " Constant
meas_flag_MLY-TMIN-NORMAL has constant value " " Constant
meas_flag_MLY-DUTR-NORMAL has constant value " " Constant
MLY-TAVG-NORMAL is highly overall correlated with MLY-TMAX-NORMAL and 1 other fieldsHigh correlation
MLY-TMAX-NORMAL is highly overall correlated with MLY-TAVG-NORMAL and 1 other fieldsHigh correlation
MLY-TMIN-NORMAL is highly overall correlated with MLY-TAVG-NORMAL and 1 other fieldsHigh correlation
comp_flag_MLY-DUTR-NORMAL is highly overall correlated with comp_flag_MLY-TAVG-NORMAL and 6 other fieldsHigh correlation
comp_flag_MLY-TAVG-NORMAL is highly overall correlated with comp_flag_MLY-DUTR-NORMAL and 6 other fieldsHigh correlation
comp_flag_MLY-TMAX-NORMAL is highly overall correlated with comp_flag_MLY-DUTR-NORMAL and 6 other fieldsHigh correlation
comp_flag_MLY-TMIN-NORMAL is highly overall correlated with comp_flag_MLY-DUTR-NORMAL and 6 other fieldsHigh correlation
years_MLY-DUTR-NORMAL is highly overall correlated with comp_flag_MLY-DUTR-NORMAL and 6 other fieldsHigh correlation
years_MLY-TAVG-NORMAL is highly overall correlated with comp_flag_MLY-DUTR-NORMAL and 6 other fieldsHigh correlation
years_MLY-TMAX-NORMAL is highly overall correlated with comp_flag_MLY-DUTR-NORMAL and 6 other fieldsHigh correlation
years_MLY-TMIN-NORMAL is highly overall correlated with comp_flag_MLY-DUTR-NORMAL and 6 other fieldsHigh correlation

Reproduction

Analysis started2024-11-19 09:22:10.520701
Analysis finished2024-11-19 09:22:19.265989
Duration8.75 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Distinct7306
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
2024-11-19T03:22:19.416777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters964392
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAW00064757
2nd rowCAW00064757
3rd rowCAW00064757
4th rowCAW00064757
5th rowCAW00064757
ValueCountFrequency (%)
fmc00914843 12
 
< 0.1%
usw00023119 12
 
< 0.1%
caw00064757 12
 
< 0.1%
cqc00914080 12
 
< 0.1%
cqc00914801 12
 
< 0.1%
cqc00914855 12
 
< 0.1%
fmc00914395 12
 
< 0.1%
fmc00914419 12
 
< 0.1%
fmc00914446 12
 
< 0.1%
fmc00914720 12
 
< 0.1%
Other values (7296) 87552
99.9%
2024-11-19T03:22:19.675734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 251448
26.1%
S 87012
 
9.0%
U 86976
 
9.0%
C 73608
 
7.6%
4 65628
 
6.8%
1 62340
 
6.5%
2 60144
 
6.2%
3 59496
 
6.2%
5 46632
 
4.8%
8 40404
 
4.2%
Other values (13) 130704
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 964392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 251448
26.1%
S 87012
 
9.0%
U 86976
 
9.0%
C 73608
 
7.6%
4 65628
 
6.8%
1 62340
 
6.5%
2 60144
 
6.2%
3 59496
 
6.2%
5 46632
 
4.8%
8 40404
 
4.2%
Other values (13) 130704
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 964392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 251448
26.1%
S 87012
 
9.0%
U 86976
 
9.0%
C 73608
 
7.6%
4 65628
 
6.8%
1 62340
 
6.5%
2 60144
 
6.2%
3 59496
 
6.2%
5 46632
 
4.8%
8 40404
 
4.2%
Other values (13) 130704
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 964392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 251448
26.1%
S 87012
 
9.0%
U 86976
 
9.0%
C 73608
 
7.6%
4 65628
 
6.8%
1 62340
 
6.5%
2 60144
 
6.2%
3 59496
 
6.2%
5 46632
 
4.8%
8 40404
 
4.2%
Other values (13) 130704
13.6%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size685.1 KiB
2024-11-19T03:22:19.754267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4520722
Coefficient of variation (CV)0.53108803
Kurtosis-1.2167842
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum569868
Variance11.916803
MonotonicityNot monotonic
2024-11-19T03:22:19.840952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 7306
8.3%
2 7306
8.3%
3 7306
8.3%
4 7306
8.3%
5 7306
8.3%
6 7306
8.3%
7 7306
8.3%
8 7306
8.3%
9 7306
8.3%
10 7306
8.3%
Other values (2) 14612
16.7%
ValueCountFrequency (%)
1 7306
8.3%
2 7306
8.3%
3 7306
8.3%
4 7306
8.3%
5 7306
8.3%
6 7306
8.3%
7 7306
8.3%
8 7306
8.3%
9 7306
8.3%
10 7306
8.3%
ValueCountFrequency (%)
12 7306
8.3%
11 7306
8.3%
10 7306
8.3%
9 7306
8.3%
8 7306
8.3%
7 7306
8.3%
6 7306
8.3%
5 7306
8.3%
4 7306
8.3%
3 7306
8.3%

day
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
99
87672 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters175344
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99
2nd row99
3rd row99
4th row99
5th row99

Common Values

ValueCountFrequency (%)
99 87672
100.0%

Length

2024-11-19T03:22:19.924437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:19.991457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
99 87672
100.0%

Most occurring characters

ValueCountFrequency (%)
9 175344
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 175344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 175344
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 175344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 175344
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 175344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 175344
100.0%

hour
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
99
87672 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters175344
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99
2nd row99
3rd row99
4th row99
5th row99

Common Values

ValueCountFrequency (%)
99 87672
100.0%

Length

2024-11-19T03:22:20.065046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:20.133034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
99 87672
100.0%

Most occurring characters

ValueCountFrequency (%)
9 175344
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 175344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 175344
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 175344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 175344
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 175344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 175344
100.0%

MLY-TAVG-NORMAL
Real number (ℝ)

High correlation 

Distinct1055
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.423023
Minimum-20
Maximum104.2
Zeros3
Zeros (%)< 0.1%
Negative166
Negative (%)0.2%
Memory size685.1 KiB
2024-11-19T03:22:20.208556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-20
5-th percentile22.9
Q140
median54.9
Q368.2
95-th percentile80.1
Maximum104.2
Range124.2
Interquartile range (IQR)28.2

Descriptive statistics

Standard deviation18.115154
Coefficient of variation (CV)0.3390889
Kurtosis-0.61483805
Mean53.423023
Median Absolute Deviation (MAD)14
Skewness-0.31580932
Sum4683703.3
Variance328.1588
MonotonicityNot monotonic
2024-11-19T03:22:20.316636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.1 208
 
0.2%
68.9 205
 
0.2%
69 203
 
0.2%
68.4 198
 
0.2%
67.5 192
 
0.2%
73.1 192
 
0.2%
66.9 191
 
0.2%
67.8 191
 
0.2%
68 191
 
0.2%
66.2 190
 
0.2%
Other values (1045) 85711
97.8%
ValueCountFrequency (%)
-20 1
 
< 0.1%
-15.9 1
 
< 0.1%
-15.8 1
 
< 0.1%
-15.7 3
< 0.1%
-15.6 2
< 0.1%
-15.4 1
 
< 0.1%
-15.1 1
 
< 0.1%
-15 1
 
< 0.1%
-14.9 3
< 0.1%
-14.8 1
 
< 0.1%
ValueCountFrequency (%)
104.2 1
< 0.1%
102.3 2
< 0.1%
100.1 1
< 0.1%
98.5 1
< 0.1%
97.5 1
< 0.1%
97.4 1
< 0.1%
96.9 1
< 0.1%
96 1
< 0.1%
95.9 2
< 0.1%
95.8 1
< 0.1%

meas_flag_MLY-TAVG-NORMAL
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
87672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87672
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
87672
100.0%

Length

2024-11-19T03:22:20.408715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:20.481928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87672
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
87672
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
87672
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
87672
100.0%

comp_flag_MLY-TAVG-NORMAL
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
R
49884 
S
31960 
E
5479 
P
 
349

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87672
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

Length

2024-11-19T03:22:20.550269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:20.632367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
r 49884
56.9%
s 31960
36.5%
e 5479
 
6.2%
p 349
 
0.4%

Most occurring characters

ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

years_MLY-TAVG-NORMAL
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.402728
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size685.1 KiB
2024-11-19T03:22:20.703454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q115
median21
Q326
95-th percentile30
Maximum30
Range28
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.6608688
Coefficient of variation (CV)0.32646951
Kurtosis-0.79329035
Mean20.402728
Median Absolute Deviation (MAD)6
Skewness-0.38625115
Sum1788748
Variance44.367173
MonotonicityNot monotonic
2024-11-19T03:22:20.782694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
28 5939
 
6.8%
27 5144
 
5.9%
30 5060
 
5.8%
26 4963
 
5.7%
29 4495
 
5.1%
25 4372
 
5.0%
23 4292
 
4.9%
24 4214
 
4.8%
22 4044
 
4.6%
21 4001
 
4.6%
Other values (19) 41148
46.9%
ValueCountFrequency (%)
2 259
 
0.3%
3 302
 
0.3%
4 408
 
0.5%
5 357
 
0.4%
6 517
 
0.6%
7 708
 
0.8%
8 865
 
1.0%
9 810
 
0.9%
10 2415
2.8%
11 2785
3.2%
ValueCountFrequency (%)
30 5060
5.8%
29 4495
5.1%
28 5939
6.8%
27 5144
5.9%
26 4963
5.7%
25 4372
5.0%
24 4214
4.8%
23 4292
4.9%
22 4044
4.6%
21 4001
4.6%

MLY-TMAX-NORMAL
Real number (ℝ)

High correlation 

Distinct1111
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.893857
Minimum-11.4
Maximum117.4
Zeros1
Zeros (%)< 0.1%
Negative61
Negative (%)0.1%
Memory size685.1 KiB
2024-11-19T03:22:20.861418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-11.4
5-th percentile32.1
Q150.8
median67.1
Q380.4
95-th percentile91
Maximum117.4
Range128.8
Interquartile range (IQR)29.6

Descriptive statistics

Standard deviation18.891748
Coefficient of variation (CV)0.29111767
Kurtosis-0.59837476
Mean64.893857
Median Absolute Deviation (MAD)14.4
Skewness-0.3974611
Sum5689374.2
Variance356.89816
MonotonicityNot monotonic
2024-11-19T03:22:20.956585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.1 220
 
0.3%
79.7 219
 
0.2%
81.8 212
 
0.2%
82 210
 
0.2%
80.5 207
 
0.2%
78.9 206
 
0.2%
79.1 206
 
0.2%
79.8 205
 
0.2%
79.9 205
 
0.2%
79.6 204
 
0.2%
Other values (1101) 85578
97.6%
ValueCountFrequency (%)
-11.4 1
< 0.1%
-9.9 1
< 0.1%
-9.2 1
< 0.1%
-9.1 1
< 0.1%
-8.7 1
< 0.1%
-8.6 2
< 0.1%
-8.5 2
< 0.1%
-8.2 1
< 0.1%
-8 1
< 0.1%
-7.7 1
< 0.1%
ValueCountFrequency (%)
117.4 1
< 0.1%
115.9 1
< 0.1%
114.9 1
< 0.1%
113 1
< 0.1%
111.9 1
< 0.1%
111.1 1
< 0.1%
110.8 1
< 0.1%
110.7 1
< 0.1%
110.5 1
< 0.1%
109.8 1
< 0.1%

meas_flag_MLY-TMAX-NORMAL
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
87672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87672
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
87672
100.0%

Length

2024-11-19T03:22:21.050188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:21.112711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87672
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
87672
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
87672
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
87672
100.0%

comp_flag_MLY-TMAX-NORMAL
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
R
42738 
S
39522 
E
5148 
P
 
264

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87672
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 42738
48.7%
S 39522
45.1%
E 5148
 
5.9%
P 264
 
0.3%

Length

2024-11-19T03:22:21.176568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:21.240727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
r 42738
48.7%
s 39522
45.1%
e 5148
 
5.9%
p 264
 
0.3%

Most occurring characters

ValueCountFrequency (%)
R 42738
48.7%
S 39522
45.1%
E 5148
 
5.9%
P 264
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 42738
48.7%
S 39522
45.1%
E 5148
 
5.9%
P 264
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 42738
48.7%
S 39522
45.1%
E 5148
 
5.9%
P 264
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 42738
48.7%
S 39522
45.1%
E 5148
 
5.9%
P 264
 
0.3%

years_MLY-TMAX-NORMAL
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.349473
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size685.1 KiB
2024-11-19T03:22:21.317770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q116
median22
Q328
95-th percentile30
Maximum30
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8536832
Coefficient of variation (CV)0.32102353
Kurtosis-0.75211637
Mean21.349473
Median Absolute Deviation (MAD)6
Skewness-0.50257033
Sum1871751
Variance46.972973
MonotonicityNot monotonic
2024-11-19T03:22:21.396373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
30 9310
 
10.6%
28 6654
 
7.6%
29 6629
 
7.6%
27 5041
 
5.7%
26 4381
 
5.0%
23 3990
 
4.6%
22 3850
 
4.4%
25 3818
 
4.4%
24 3716
 
4.2%
21 3559
 
4.1%
Other values (19) 36724
41.9%
ValueCountFrequency (%)
2 253
 
0.3%
3 284
 
0.3%
4 389
 
0.4%
5 308
 
0.4%
6 436
 
0.5%
7 581
 
0.7%
8 776
 
0.9%
9 935
1.1%
10 2219
2.5%
11 2205
2.5%
ValueCountFrequency (%)
30 9310
10.6%
29 6629
7.6%
28 6654
7.6%
27 5041
5.7%
26 4381
5.0%
25 3818
4.4%
24 3716
 
4.2%
23 3990
4.6%
22 3850
4.4%
21 3559
 
4.1%

MLY-TMIN-NORMAL
Real number (ℝ)

High correlation 

Distinct1036
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.949435
Minimum-28.6
Maximum91
Zeros6
Zeros (%)< 0.1%
Negative614
Negative (%)0.7%
Memory size685.1 KiB
2024-11-19T03:22:21.490357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-28.6
5-th percentile12.6
Q128.9
median42.4
Q355.9
95-th percentile69.7
Maximum91
Range119.6
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.763349
Coefficient of variation (CV)0.42344667
Kurtosis-0.58420462
Mean41.949435
Median Absolute Deviation (MAD)13.5
Skewness-0.18877503
Sum3677790.9
Variance315.53655
MonotonicityNot monotonic
2024-11-19T03:22:21.571148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.2 197
 
0.2%
40.8 197
 
0.2%
47.9 193
 
0.2%
34.6 192
 
0.2%
42.6 191
 
0.2%
38.4 190
 
0.2%
33.6 190
 
0.2%
40.2 189
 
0.2%
31.9 189
 
0.2%
40.9 189
 
0.2%
Other values (1026) 85755
97.8%
ValueCountFrequency (%)
-28.6 1
< 0.1%
-25.9 1
< 0.1%
-24 1
< 0.1%
-23 1
< 0.1%
-22.9 1
< 0.1%
-22.8 1
< 0.1%
-22.6 1
< 0.1%
-22.2 1
< 0.1%
-22.1 1
< 0.1%
-22 2
< 0.1%
ValueCountFrequency (%)
91 1
< 0.1%
89.8 1
< 0.1%
88.7 1
< 0.1%
87.3 1
< 0.1%
86.6 1
< 0.1%
85.4 1
< 0.1%
84.5 1
< 0.1%
84.3 1
< 0.1%
84 1
< 0.1%
83.6 1
< 0.1%

meas_flag_MLY-TMIN-NORMAL
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
87672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87672
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
87672
100.0%

Length

2024-11-19T03:22:21.788752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:21.853754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87672
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
87672
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
87672
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
87672
100.0%

comp_flag_MLY-TMIN-NORMAL
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
R
45736 
S
36498 
E
5148 
P
 
290

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87672
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 45736
52.2%
S 36498
41.6%
E 5148
 
5.9%
P 290
 
0.3%

Length

2024-11-19T03:22:21.915027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:21.978452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
r 45736
52.2%
s 36498
41.6%
e 5148
 
5.9%
p 290
 
0.3%

Most occurring characters

ValueCountFrequency (%)
R 45736
52.2%
S 36498
41.6%
E 5148
 
5.9%
P 290
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 45736
52.2%
S 36498
41.6%
E 5148
 
5.9%
P 290
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 45736
52.2%
S 36498
41.6%
E 5148
 
5.9%
P 290
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 45736
52.2%
S 36498
41.6%
E 5148
 
5.9%
P 290
 
0.3%

years_MLY-TMIN-NORMAL
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.818836
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size685.1 KiB
2024-11-19T03:22:22.056672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q115
median22
Q327
95-th percentile30
Maximum30
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.7395333
Coefficient of variation (CV)0.32372287
Kurtosis-0.78285983
Mean20.818836
Median Absolute Deviation (MAD)5
Skewness-0.43059713
Sum1825229
Variance45.421309
MonotonicityNot monotonic
2024-11-19T03:22:22.136861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
30 6726
 
7.7%
28 6093
 
6.9%
29 5586
 
6.4%
27 5108
 
5.8%
26 4678
 
5.3%
25 4237
 
4.8%
23 4204
 
4.8%
24 4094
 
4.7%
22 3992
 
4.6%
21 3946
 
4.5%
Other values (19) 39008
44.5%
ValueCountFrequency (%)
2 257
 
0.3%
3 296
 
0.3%
4 396
 
0.5%
5 322
 
0.4%
6 452
 
0.5%
7 592
 
0.7%
8 824
 
0.9%
9 863
 
1.0%
10 2376
2.7%
11 2566
2.9%
ValueCountFrequency (%)
30 6726
7.7%
29 5586
6.4%
28 6093
6.9%
27 5108
5.8%
26 4678
5.3%
25 4237
4.8%
24 4094
4.7%
23 4204
4.8%
22 3992
4.6%
21 3946
4.5%

MLY-DUTR-NORMAL
Real number (ℝ)

Distinct421
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.944295
Minimum4.9
Maximum51.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size685.1 KiB
2024-11-19T03:22:22.229460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile14.1
Q119.5
median22.5
Q325.9
95-th percentile33.4
Maximum51.4
Range46.5
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation5.6745502
Coefficient of variation (CV)0.24731857
Kurtosis0.75573307
Mean22.944295
Median Absolute Deviation (MAD)3.2
Skewness0.45129211
Sum2011572.2
Variance32.20052
MonotonicityNot monotonic
2024-11-19T03:22:22.308395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 910
 
1.0%
22.8 859
 
1.0%
21.3 849
 
1.0%
22.2 837
 
1.0%
21.5 833
 
1.0%
21.6 827
 
0.9%
21.9 816
 
0.9%
22.3 809
 
0.9%
22.7 809
 
0.9%
22.5 808
 
0.9%
Other values (411) 79315
90.5%
ValueCountFrequency (%)
4.9 1
 
< 0.1%
5.3 1
 
< 0.1%
5.6 1
 
< 0.1%
5.7 1
 
< 0.1%
5.8 1
 
< 0.1%
5.9 3
< 0.1%
6 2
 
< 0.1%
6.1 5
< 0.1%
6.2 2
 
< 0.1%
6.3 4
< 0.1%
ValueCountFrequency (%)
51.4 1
< 0.1%
50.6 1
< 0.1%
49.6 1
< 0.1%
48.3 1
< 0.1%
47.8 1
< 0.1%
47.7 1
< 0.1%
47.6 1
< 0.1%
47.5 1
< 0.1%
47.3 2
< 0.1%
47 2
< 0.1%

meas_flag_MLY-DUTR-NORMAL
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
87672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87672
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
87672
100.0%

Length

2024-11-19T03:22:22.402730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:22.465680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87672
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
87672
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
87672
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
87672
100.0%

comp_flag_MLY-DUTR-NORMAL
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.1 KiB
R
49884 
S
31960 
E
5479 
P
 
349

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87672
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

Length

2024-11-19T03:22:22.536163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-19T03:22:22.591543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
r 49884
56.9%
s 31960
36.5%
e 5479
 
6.2%
p 349
 
0.4%

Most occurring characters

ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 49884
56.9%
S 31960
36.5%
E 5479
 
6.2%
P 349
 
0.4%

years_MLY-DUTR-NORMAL
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.402728
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size685.1 KiB
2024-11-19T03:22:22.670058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q115
median21
Q326
95-th percentile30
Maximum30
Range28
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.6608688
Coefficient of variation (CV)0.32646951
Kurtosis-0.79329035
Mean20.402728
Median Absolute Deviation (MAD)6
Skewness-0.38625115
Sum1788748
Variance44.367173
MonotonicityNot monotonic
2024-11-19T03:22:22.748580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
28 5939
 
6.8%
27 5144
 
5.9%
30 5060
 
5.8%
26 4963
 
5.7%
29 4495
 
5.1%
25 4372
 
5.0%
23 4292
 
4.9%
24 4214
 
4.8%
22 4044
 
4.6%
21 4001
 
4.6%
Other values (19) 41148
46.9%
ValueCountFrequency (%)
2 259
 
0.3%
3 302
 
0.3%
4 408
 
0.5%
5 357
 
0.4%
6 517
 
0.6%
7 708
 
0.8%
8 865
 
1.0%
9 810
 
0.9%
10 2415
2.8%
11 2785
3.2%
ValueCountFrequency (%)
30 5060
5.8%
29 4495
5.1%
28 5939
6.8%
27 5144
5.9%
26 4963
5.7%
25 4372
5.0%
24 4214
4.8%
23 4292
4.9%
22 4044
4.6%
21 4001
4.6%

Interactions

2024-11-19T03:22:17.896380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.318034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.987756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.688569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.357457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.053490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.845460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.517701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.190881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.989565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.378224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.065536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.757355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.425748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.123596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.916434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.594199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.269971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:18.068810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.453617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.150458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.830967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.512963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.327754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.999220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.674116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.352253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:18.139392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.537608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.230148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.906037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.592942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.397566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.076060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.744483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.429618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:18.218560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.603105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.306484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.980681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.672834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.466219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.157310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.818291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.516521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:18.295475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.671631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.381634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.060325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.755667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.545862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.229893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.889210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.596000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:18.376250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.761688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.457651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.136925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.831793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.618827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.304541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.964782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.670794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:18.458834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.831701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.541277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.214308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.898713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.690647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.379240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.035764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.750406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:18.669071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:12.906495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:13.617007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.274997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:14.976215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:15.756170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:16.449164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.109488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-19T03:22:17.826405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-19T03:22:22.811315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
MLY-DUTR-NORMALMLY-TAVG-NORMALMLY-TMAX-NORMALMLY-TMIN-NORMALcomp_flag_MLY-DUTR-NORMALcomp_flag_MLY-TAVG-NORMALcomp_flag_MLY-TMAX-NORMALcomp_flag_MLY-TMIN-NORMALmonthyears_MLY-DUTR-NORMALyears_MLY-TAVG-NORMALyears_MLY-TMAX-NORMALyears_MLY-TMIN-NORMAL
MLY-DUTR-NORMAL1.0000.1890.3140.0630.0890.0890.0820.0810.001-0.025-0.0250.006-0.032
MLY-TAVG-NORMAL0.1891.0000.9890.9890.0920.0920.0860.0820.186-0.036-0.036-0.034-0.028
MLY-TMAX-NORMAL0.3140.9891.0000.9560.0680.0680.0570.0590.181-0.037-0.037-0.030-0.030
MLY-TMIN-NORMAL0.0630.9890.9561.0000.1040.1040.0940.0920.189-0.034-0.034-0.035-0.024
comp_flag_MLY-DUTR-NORMAL0.0890.0920.0680.1041.0001.0000.8850.9190.0000.7180.7180.6850.702
comp_flag_MLY-TAVG-NORMAL0.0890.0920.0680.1041.0001.0000.8850.9190.0000.7180.7180.6850.702
comp_flag_MLY-TMAX-NORMAL0.0820.0860.0570.0940.8850.8851.0000.8960.0000.7010.7010.7250.685
comp_flag_MLY-TMIN-NORMAL0.0810.0820.0590.0920.9190.9190.8961.0000.0000.7130.7130.6820.724
month0.0010.1860.1810.1890.0000.0000.0000.0001.000-0.008-0.008-0.009-0.008
years_MLY-DUTR-NORMAL-0.025-0.036-0.037-0.0340.7180.7180.7010.713-0.0081.0001.0000.9620.985
years_MLY-TAVG-NORMAL-0.025-0.036-0.037-0.0340.7180.7180.7010.713-0.0081.0001.0000.9620.985
years_MLY-TMAX-NORMAL0.006-0.034-0.030-0.0350.6850.6850.7250.682-0.0090.9620.9621.0000.934
years_MLY-TMIN-NORMAL-0.032-0.028-0.030-0.0240.7020.7020.6850.724-0.0080.9850.9850.9341.000

Missing values

2024-11-19T03:22:18.788389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-19T03:22:19.050584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GHCN_IDmonthdayhourMLY-TAVG-NORMALmeas_flag_MLY-TAVG-NORMALcomp_flag_MLY-TAVG-NORMALyears_MLY-TAVG-NORMALMLY-TMAX-NORMALmeas_flag_MLY-TMAX-NORMALcomp_flag_MLY-TMAX-NORMALyears_MLY-TMAX-NORMALMLY-TMIN-NORMALmeas_flag_MLY-TMIN-NORMALcomp_flag_MLY-TMIN-NORMALyears_MLY-TMIN-NORMALMLY-DUTR-NORMALmeas_flag_MLY-DUTR-NORMALcomp_flag_MLY-DUTR-NORMALyears_MLY-DUTR-NORMAL
0CAW000647571999919.1R1626.6R1611.5R1615.1R16
1CAW000647572999920.5R1628.5R1612.6R1615.8R16
2CAW000647573999929.3R1637.5R1621.1R1616.4R16
3CAW000647574999941.4R1651.2R1631.5R1619.7R16
4CAW000647575999953.9R1664.9R1642.9R1622.0R16
5CAW000647576999963.1R1673.7R1652.5R1621.2R16
6CAW000647577999967.7R1678.1R1657.2R1620.9R16
7CAW000647578999966.0R1776.6R1755.3R1721.3R17
8CAW000647579999959.0R1769.5R1748.6R1720.9R17
9CAW0006475710999947.1R1756.0R1738.3R1717.7R17
GHCN_IDmonthdayhourMLY-TAVG-NORMALmeas_flag_MLY-TAVG-NORMALcomp_flag_MLY-TAVG-NORMALyears_MLY-TAVG-NORMALMLY-TMAX-NORMALmeas_flag_MLY-TMAX-NORMALcomp_flag_MLY-TMAX-NORMALyears_MLY-TMAX-NORMALMLY-TMIN-NORMALmeas_flag_MLY-TMIN-NORMALcomp_flag_MLY-TMIN-NORMALyears_MLY-TMIN-NORMALMLY-DUTR-NORMALmeas_flag_MLY-DUTR-NORMALcomp_flag_MLY-DUTR-NORMALyears_MLY-DUTR-NORMAL
87662USW000231193999955.5E970.8E940.2E930.6E9
87663USW000231194999959.1E973.9E944.2E929.7E9
87664USW000231195999965.4E979.9E950.8E929.1E9
87665USW000231196999971.4E987.2E955.6E931.6E9
87666USW000231197999977.9E894.1E861.6E832.5E8
87667USW000231198999978.3E994.9E961.7E933.2E9
87668USW000231199999975.1E1091.5E1058.7E1032.8E10
87669USW0002311910999965.5E1081.9E1049.1E1032.8E10
87670USW0002311911999956.5E1072.3E1040.7E1031.6E10
87671USW0002311912999951.0E964.8E937.1E927.7E9